A practical input-to-state stability certificate for Koopman learning control is derived, separating prediction residuals from selected-channel margins and projection residuals.
Algorithmic Learning in a Random World
22 Pith papers cite this work, alongside 1,049 external citations. Polarity classification is still indexing.
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SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
ActProbe is an action-space detector that uses temporal consistency error and action chunk magnitude from policy outputs, mapped via LSTM-MLP, to predict failures earlier than baselines across policies and real-robot tasks.
An adaptive two-phase semantic filter using clustering then a hybrid proxy trained on LLM confidence achieves 1.6-2.0x speedup over prior methods at 90% accuracy on 10K document corpora.
AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
PLACE delivers a closed-form certified classification method for point clouds and graphs based on persistent homology with explicit excess-risk bounds, selection rules, and training-time certificates.
Foresight detects failures in long-horizon robotic manipulation using latents from action-conditioned world models trained only on task-level labels and calibrated via functional conformal prediction.
QUEST measures uncertainty via the Lebesgue volume of highest-density regions of a distribution's support, evaluated at robustness parameter alpha, and claims to satisfy UQ axioms while outperforming variance and differential entropy on selective prediction tasks.
MARD-7B outperforms baselines and GPT-4o on novel drug pairs for mechanism-level DDI prediction via a new distillation pipeline with verifiable process rewards and releases all resources.
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
TA-CQR estimates a lower-tail allocation to shorten conformal intervals, but the abstract's regular-variation length formula is absent from the full text.
Bayesian deep learning method rankings are unreliable under data scarcity, reversing across datasets and sample sizes, and a hierarchical Bayesian framework with predictive detectability curves is needed to assess evaluation sufficiency.
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
DPGR framework infers clade-level relative transmission fitness from GISAID influenza data and CNN models predict DPGR from full viral genomes with R² above 0.95.
Instrumented data augments observations with mechanistic models, uncertainty, and counterfactuals to enable causal interventions via Pearl's do-operator in scientific machine learning.
Bayesian deep learning method rankings are unstable at small sample sizes, dataset-dependent, and require uncertainty-aware evaluation using hierarchical models and minimum detectable difference curves.
CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.
Compares Delta method, Bayesian Monte Carlo Dropout, Bootstrap, Lower-Upper Bound Estimation, and Mean-Variance Estimation for prediction intervals on turbine gas temperature data using coverage probability, normalized mean prediction interval width, and coverage width-based criterion.
citing papers explorer
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Input-to-State Stability Certification via Projection Residuals for Koopman Learning Control of Nonlinear Repetitive Systems
A practical input-to-state stability certificate for Koopman learning control is derived, separating prediction residuals from selected-channel margins and projection residuals.
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Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
-
ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
ActProbe is an action-space detector that uses temporal consistency error and action chunk magnitude from policy outputs, mapped via LSTM-MLP, to predict failures earlier than baselines across policies and real-robot tasks.
-
Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method
An adaptive two-phase semantic filter using clustering then a hybrid proxy trained on LLM confidence achieves 1.6-2.0x speedup over prior methods at 90% accuracy on 10K document corpora.
-
Planner-Admissible Graph-PDE Value Extensions for Sparse Goal-Conditioned Planning
AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
-
SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
-
A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification
PLACE delivers a closed-form certified classification method for point clouds and graphs based on persistent homology with explicit excess-risk bounds, selection rules, and training-time certificates.
-
Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents
Foresight detects failures in long-horizon robotic manipulation using latents from action-conditioned world models trained only on task-level labels and calibrated via functional conformal prediction.
-
On the QUEST for Uncertainty Quantification via Highest Density Regions
QUEST measures uncertainty via the Lebesgue volume of highest-density regions of a distribution's support, evaluated at robustness parameter alpha, and claims to satisfy UQ axioms while outperforming variance and differential entropy on selective prediction tasks.
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MARD: Mirror-Augmented Reasoning Distillation for Mechanism-Level Drug-Drug Interaction Prediction
MARD-7B outperforms baselines and GPT-4o on novel drug pairs for mechanism-level DDI prediction via a new distillation pipeline with verifiable process rewards and releases all resources.
-
Skew-adaptive conformal prediction
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
-
RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
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Learning Polyhedral Conformal Sets for Robust Optimization
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
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Geometry of tail allocation in conformal prediction intervals
TA-CQR estimates a lower-tail allocation to shorten conformal intervals, but the abstract's regular-variation length formula is absent from the full text.
-
ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
Bayesian deep learning method rankings are unreliable under data scarcity, reversing across datasets and sample sizes, and a hierarchical Bayesian framework with predictive detectability curves is needed to assess evaluation sufficiency.
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Context Kubernetes: Declarative Orchestration of Enterprise Knowledge for Agentic AI Systems
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
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Inferring and Predicting Clade-Level Relative Transmission Fitness in Seasonal Influenza A Using Differential Population Growth Rate and Deep Learning
DPGR framework infers clade-level relative transmission fitness from GISAID influenza data and CNN models predict DPGR from full viral genomes with R² above 0.95.
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Instrumented data for causal scientific machine learning
Instrumented data augments observations with mechanistic models, uncertainty, and counterfactuals to enable causal interventions via Pearl's do-operator in scientific machine learning.
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Unstable Rankings in Bayesian Deep Learning Evaluation
Bayesian deep learning method rankings are unstable at small sample sizes, dataset-dependent, and require uncertainty-aware evaluation using hierarchical models and minimum detectable difference curves.
-
Uncertainty-Aware Transformers: Conformal Prediction for Language Models
CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.
-
Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation
Compares Delta method, Bayesian Monte Carlo Dropout, Bootstrap, Lower-Upper Bound Estimation, and Mean-Variance Estimation for prediction intervals on turbine gas temperature data using coverage probability, normalized mean prediction interval width, and coverage width-based criterion.
- How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective